Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment

TL;DR

This paper introduces DIMVC-HIA, a deep learning framework for incomplete multi-view clustering that effectively imputes missing data and aligns semantic features across views, improving clustering accuracy.

Contribution

The paper proposes a novel hierarchical imputation and alignment framework that jointly addresses missing data imputation and semantic consistency in multi-view clustering.

Findings

Achieves superior clustering performance on benchmark datasets.

Effectively handles varying levels of missing data.

Enhances cross-view consistency and intra-cluster compactness.

Abstract

Incomplete multi-view clustering (IMVC) aims to discover shared cluster structures from multi-view data with partial observations. The core challenges lie in accurately imputing missing views without introducing bias, while maintaining semantic consistency across views and compactness within clusters. To address these challenges, we propose DIMVC-HIA, a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components: (1) view-specific autoencoders for latent feature extraction, coupled with a view-shared clustering predictor to produce soft cluster assignments; (2) a hierarchical imputation module that first estimates missing cluster assignments based on cross-view contrastive similarity, and then reconstructs missing features using intra-view, intra-cluster statistics; (3) an energy-based semantic alignment module, which promotes intra-cluster compactness by minimizing energy variance around low-energy cluster anchors; and (4) a contrastive assignment alignment module, which enhances cross-view consistency and encourages confident, well-separated cluster predictions. Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.